Mathematical Modeling of Linear Static and Dynamic Analysis for Pier Height Effect on the Structural Performance of Bridges Structures
Bibliographic record
Abstract
The results of linear static analysis explained that the increasing of pier heights was leaded to rise the values of positive bending moment, tensile stresses, and downward vertical deflection. Whereas the compressive stresses and negative bending moment were decreased, indicating that the structural performance of bridge structure representing by stiffness, bearing capacity of structural members, and elasticity will decrease and the bridges structures will be damaged. Therefore, the bridges structures need safe design when using tall piers by adopting high quality materials such as high strength concrete, more steel reinforcement, more prestressed tendons, and increasing of cross section dimensions of girders and piers. The results of modal analysis show that the un-loaded dynamic frequency for three types of bridges models were decreased when the pier heights were increased, indicating that the stiffness of bridges structure was became low with higher pier height. According to response spectra and time history analysis results, the loaded dynamic frequency (vibration state) and dynamic displacement were increased when the pier heights were increased, showing that the bridge of structure will suffer from high vibration when the pier height was high. It can be concluded that from this study, the piers heights have significant effects on the static and dynamic structural performance of bridges structures under traffic loads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".